A Comparative Evaluation of Different Techniques of Supervised Classification in Landuse/Landcover Mapping of Awka South L.G.A, Anambra State, Nigeria.

J.o Ejikeme ,Josephine Emengini ,Elizabeth Ugwu ,Daniel Umenweke

doi:10.7176/jees/9-5-10

Abstract

The aim of this study is to compare the different techniques of supervised classification using Awka South LGA, of Anambra State as a case study. The techniques considered include: Maximum Likelihood (MLC), Minimum Distance, Mahalanobis Distance, Spectral Angle Mapper and Parallelepiped. Landsat 7 ETM+ (2000 and 2007) and Landsat 8 OLI/TIRS (2015) were acquired. The images were pre-processed. The scan-line effect present in the Landsat 7 image was corrected using the analysis tool of Quantum GIS (QGIS) 2.18 software. To compensate for atmospheric effects, Fast Line-of-site Atmospheric Analysis of Hypercube (FLAASH) Atmospheric Module of ENVI software was used. Image enhancement was carried out on the images. The images were classified using the different techniques and the results compared. Change detection was also carried out to determine the rate of changes between 2000 and 2015. Error matrices of the various techniques were calculated to determine the accuracy level of the algorithms and to judge which is the better choice. It can be deduced from the results that Maximum Likelihood (99.63%) produced the best result, followed closely by Mahalanobis Distance (98.54%), Spectral Angle (89.28%), Minimum Distance (84.42%) and Parallelepiped (85.00%). The study recommends Maximum Likelihood Classification algorithm for supervised classification. Key words: Classification, Maximum Likelihood, Algorithm, Land cover land use DOI : 10.7176/JEES/9-5-10 Publication date :May 31 st 2019

Highlights

Classification of Satellite Images is a key component for various Object Recognition Systems and Automatic Thematic Map Generation Systems
Aerial image of the study area was acquired for object based classification which was used as a reference for the comparison. 3.2 Data Processing 3.2.1 Scan-Line Correction Gap Filling On May 31, 2003, the Scan Line Corrector (SLC), which compensates for the forward motion of Landsat 7, failed
The software used for this project is Quantum GIS (QGIS) 2.18, the software used the Gap Mask images provided in the zipped file of the Landsat 7 image

Summary

Introduction

Classification of Satellite Images is a key component for various Object Recognition Systems and Automatic Thematic Map Generation Systems. Image classification is the most important part of image analysis, remote sensing and pattern recognition applications. Image classification may serve as the ultimate product while in other cases it can serve only as an intermediate step. Image classification is a significant tool for digital images analysis and object recognition. The selection of the appropriate classification technique to employ can have considerable upshot on the results of whether the classification is used as an ultimate product or as one of numerous analytical procedures applied for deriving information from an image for additional analyses (Gabrya, and Petrakieva 2004; Kalra et al 2013)

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